Driving the future of Voice: Meet Vapi's New VP of Product. Vapi Editorial Team - Aug 06, 2026 A few months ago, Nathalie Criou joined Vapi as VP of Product. Her path here runs through nearly every seat in a technology company: software engineer, marketer, seller, product manager at Google, founder, and product and general management leader at VMware, Amazon, Twilio, and Docker. She's built at every scale, from a startup she founded and sold to leading teams and functions at some of the largest tech companies in the world. She came to Vapi because she believes voice is the channel that many businesses gave up on, and that with AI, there is an opportunity to transform the way people interact with it. A few months in, Vapi sat down with her to talk about her career, why she picked Vapi, her product philosophy, and what keeps her motivated. Tell Vapi a bit about yourself and your career path leading up to Vapi. I started as a software engineer working in signal processing. I liked the work, but I realized I was too far from customers. I wanted to get closer to people actually using what I built. I wanted to understand their problems and build platforms to drive solutions. So I moved through technical marketing, then product marketing, then spent a year in technical sales. None of it was quite the right fit, so I went to business school to figure it out. That's where I found product management. After my MBA, I joined Google, where I was part of the team integrating DoubleClick after the acquisition. I also worked at AdMob, another startup Google later acquired. Then I founded my own company, RidePal, and sold it roughly four years later. From there I kept going: Apteligent, which was acquired by VMware, then VMware itself, Amazon, Twilio, and Docker. Engineering, go-to-market, founding, general management, at companies of every size. That's the arc that led me here. What made Vapi stand out when you were deciding on your next move? Three reasons. First, the opportunity in the industry is enormous. Your upside is proportional to the size of the market you're in, and the chance to have meaningful impact here is real. Second, the channel itself matters to me. Voice is inclusive, low-friction, and highly effective. But businesses created such terrible experiences around it that humans abandoned it. Now the technology exists to fix that, and being part of that reversal is compelling. Third, the founders. They have a rare combination of genuine empathy and sheer ambition. Deeply human and intensely driven at the same time. You don't find that pairing often. What excites you most about joining a company at this stage? Where do you see voice AI heading? Voice AI is one of the few AI applications with demonstrated, repeatable ROI, and it can only improve from here. Here's the thing most people miss: current call volumes are artificially low. Companies minimize them to cut costs and reduce friction. Remove that constraint and the number of conversations should explode. Anyone who wants to talk to a company should simply be able to pick up the phone. One of its customers, Kavak, is a case study in what that transformation looks like. They started by using voice AI to augment one existing use case and got immediate ROI. Then they followed the business logic and customer demand, and within six months the entire company had reorganized around voice as a primary channel. I expect that pattern to repeat at any company where voice can play a central role in how they serve customers. The companies that lean in early will find voice becoming a core channel, not a supplementary one. How do you approach setting product priorities when different types of customers need different things from the platform? Being a platform is an advantage here. Every improvement benefits all users, and there's a natural feedback loop built in. In a market this large, even missteps can turn into learnings. There's no wrong answer. Vapi historically focused on developer experience, and that drove its early growth. But developers represent roughly 1% of the workforce. The addressable audience is much larger than what Vapi has served so far. What's changing is that AI is removing the friction that used to force non-technical teams to route through marketing, email, or other filters to reach their customers. Those teams can now engage customers directly and trust the platform to protect the customer experience. That means non-technical users increasingly need to interact with the platform, which requires a higher level of abstraction. The interesting part is that less technical users vary in sophistication, but they have the same clarity about outcomes and success metrics as developers do. The product challenge is meeting them where they are without compromising what makes the platform powerful for technical users. The AI space moves fast. How do you build a roadmap when the underlying technology keeps shifting? Start with what won't change. AI will continue to grow; the train doesn't stop, and use cases will compound. What I call market physics stays constant no matter how fast AI moves. Companies still need to grow revenue or cut costs, and AI helps with both, sometimes simultaneously. As a PM, you stay anchored to customer problems: what's the single biggest growth opportunity, what's the single biggest cost to eliminate, and what's getting in the way of either. The danger is treating AI as the end goal rather than the means. The goal of technology should be to solve a customer pain point. The discipline is to stay focused on the problem and let AI be the tool to help solve it, rather than the goal in itself. Near term, the economics favor fine-tuning for specific, narrow applications. Current models are still expensive and inefficient. I expect more integration between AI systems, and likely a unified data plane for shared context across models. What's one belief you hold about building products that most PMs would disagree with? Some product leaders might actually agree, but for many it is counterintuitive: Product management is not about managing a product. It's about managing an opportunity. Organizing teams around existing products or features is a mistake, because it shifts the incentive from the problem to the solution. A team anchored to a product is incentivized to maintain and defend it, not to question whether it's still the best answer. Organize around problems instead, and teams stay relevant. They can discover bigger opportunities customers didn't know they had, and they can find better ways to solve existing problems without being constrained by what already ships. Ownership of what exists still matters, but it shouldn't be the organizing principle. What do you do outside of work that keeps you grounded? Anyone who knows me, or has talked to me for even a few minutes, will know the answer pretty quickly: sailing and cats. No hesitation on either.

Introducing Vapi Model Intelligence: model recommendations backed by production data. Vapi Editorial Team - Jul 21, 2026 To help builders get started faster and ship better agents, Vapi Inc. is launching Vapi Model Intelligence, a bundle of features that make it easier to choose the right model combination for your use case. Model Presets are curated configurations of transcribers, LLMs, and voice models that are already selected for you and tuned to specific goals, such as low latency or high intelligence. Additionally, updated model performance metrics display current cost, latency, and quality data for every model in the catalog based on Vapi production data, so you can see the trade-offs between models and be better equipped to pick the best models for your use case. Both are built on the same foundation: the production data from the calls running through Vapi every day. Here's why Vapi Inc. built it, how it works, and what it changes for you. Why Vapi Inc. built it. The number one question Vapi Inc. get from builders, in webinars, in the community, in support tickets, is the same: "Which models should I use?" Builders in the Vapi Dashboard are excited to combine and customize model configurations, but without guidance, it's hard to know which combinations actually win. And when your use case or goal changes, the best configuration changes with it. Vapi is model-agnostic: assemble an agent by choosing your own transcriber, LLM, and voice from an ever-expanding catalog of providers and models. That configurability is one of the reasons teams choose Vapi, but while the catalog grows, the range of choices can slow down decisions. New builders don't want to become experts in transcriber word error rates or TTS latency. They just want a working agent. Even power users who want to compare models can struggle without data they can trust. Vapi Inc. is in a rare position to fix that. Builders have run over 1 billion calls on Vapi with use cases spanning appointment scheduling, patient intake, collections, driver dispatch, and dozens of other real workloads. Vapi Inc. see how models actually behave in production, not in a spec sheet, and that's the data its own engineers use to select the models inside each preset. Neutrality without guidance just shifts the burden onto the builder. Model Intelligence adds the guidance: a recommendation for every use case, grounded in real deployments, plus the data to go deeper when you want it. What is Vapi Model Intelligence? One bundle, two features. Model Intelligence is a bundle that includes two features solving the same problem from different directions. Model Presets. Model Presets recommend a model selection for you, tuned to a specific goal, whether that's cost, speed, or intelligence. Each preset is a curated configuration that bundles a best-in-class transcriber, an LLM, and a voice: * Balanced. Strong across virtually any use case. This is the new platform default for all new assistants. * High Intelligence. The most capable models, for complex or high-stakes tasks where accuracy is the priority. * Ultra Fast. Optimized for the lowest latency, for use cases where speed matters most. * Cost Saver. Optimized for per-minute cost, so high-volume agents don't break the bank. Pick the preset that matches your goal and ship, without worrying that you're leaving something on the table. You never have to open a dropdown. If you do edit any component, the assistant moves to a Customized state, so presets never lock you in. Presets are also designed to be improved over time. Because Vapi stores which preset an agent is on, Vapi Inc. can update the underlying models as better ones emerge and suggest the upgrade to everyone on that preset. Nothing changes without your confirmation, so agents already in production stay put. If you opt in, your models are updated, and your configuration improves without a total rebuild. Model performance metrics. For builders in the dashboard who want control, every model now carries the data to assess it: cost, latency, and a quality metric fit to the job. For transcribers, compare Word Error Rate (accuracy). For LLMs, compare Intelligence scores. And for voices, see its proprietary Humanness Index(TM) directly on the platform: a 0 to 100 score of how human a voice model sounds in real-life deployments, as evaluated by the humans who hear them. "Sounds natural in a demo clip" and "sounds natural across thousands of live calls" are different claims, and it's a metric Vapi Inc. will use to empower builders that no other platform can surface. Benchmarks appear in cards and dropdowns, so you can compare models side by side without leaving Vapi. And performance metrics are only useful if you can trust them, so here's how they're measured: Latency is measured on live Vapi calls, not vendor specs. Most performance metrics come from controlled tests: one request, a clean network, no concurrent load, no real conversation around it. Those numbers look fast on a slide and rarely survive contact with production. Its figures are P50 medians from real traffic, so they reflect how a model actually performs in deployment. One thing Vapi Inc. learned building this: a number that looks high on paper often feels natural in a real call, so use its numbers to compare models on equal footing, then test perceived latency yourself. It is important for users considering latency to look at model performance in real, live deployments. Cost metrics for the models reflect assumptions based on typical usage in real Vapi calls, but your actual spend may vary depending on factors such as call length, complexity, prompt size, caching rates, tool responses, and more. Quality metrics vary by model type but are pulled from industry benchmarks plus Vapi's own proprietary data, including the Humanness Index for voices, a metric no other platform can surface. The data is refreshed on a regular cadence, so you're comparing current scores and metrics. The same measurements inform how its engineers select the models inside each preset, so the defaults are chosen by data, not by habit. This data is collected from hundreds of thousands of live voice agent deployments and used to empower its builders to go even further. Who is Vapi Model Intelligence for? The non-technical PM building an appointment scheduler and selects Ultra Fast to get more meetings on the books, faster. She ships a low-latency agent without ever comparing transcribers or LLMs. The healthcare team. A care coordinator handling sensitive patient intake calls needs to navigate complex situations, so the team starts on High Intelligence. Later, they compare voices by Humanness Index(TM) to find the most natural option, rather than running call after call to hear the difference. The cost-conscious operator. A high-volume feedback survey is a repetitive, low-complexity workload. Cost Saver keeps per-minute costs down without a custom build. The team without time to chase model releases. Months from now, when better models are available in the Balanced preset, they'll receive a suggested upgrade. Their agent improves without anyone rebuilding it. Everyone else. Whether you want to start from a stronger baseline or make more informed decisions when you customize, Model Intelligence meets you where you are: better defaults out of the box, and better data when you're ready to go deeper. Get started. Model Intelligence, including Model Presets and updated performance metrics, is now live in your account. New assistants start on Balanced. Switch presets or hand-pick models anytime. Pick the right models for your use case, without the guesswork.

Voice AI startup Vapi hits 00M valuation after Winning Amazon Ring. Voice AI heats up: Vapi reaches unicorn status. The voice AI market has a new unicorn. Vapi, a startup building infrastructure for AI-powered voice agents, has reportedly reached a $500 million valuation after a fiercely competitive fundraising process that saw the company fend off over 40 rival bidders. The most notable customer win driving investor enthusiasm: Amazon's Ring division, which chose Vapi to power next-generation voice interactions across its smart home security platform. Vapi's core technology enables developers to build, deploy, and scale voice AI agents that sound remarkably natural. Unlike first-generation voice bots that relied on rigid script trees, Vapi's platform leverages the latest advances in large language models and neural text-to-speech to create conversational experiences that adapt in real time. The agents can handle interruptions, understand context across long exchanges, and even detect emotional cues in a caller's voice to adjust their tone accordingly. Discover more Machine Learning Computer Science Dictionaries & Encyclopedias Winning the Amazon Ring contract was a watershed moment for the company. Ring processes millions of customer interactions daily, spanning everything from package delivery confirmations to emergency alarm responses. Deploying Vapi's voice agents across this footprint represents one of the largest real-world tests of conversational AI at scale. Early metrics reportedly show a 40% reduction in call handling time and a measurable improvement in customer satisfaction scores compared to Ring's previous interactive voice response system. Discover more AI Tools, Chatbots & Virtual Assistants Data Management Language Resources RECOMMENDED READ The Coming Wave: AI, Power, and the Greatest Dilemma of Its Age Mustafa Suleyman The definitive book on where AI is heading - written by one of the field founders. The $500 million valuation reflects broader market excitement about voice AI as a category. With improvements in speech recognition accuracy, latency reduction in streaming models, and the rise of multimodal AI that can process voice alongside text and images, the technology has crossed a threshold where it is genuinely useful for business applications. Analysts project the global voice AI market will exceed $50 billion by 2028, up from approximately $15 billion in 2025. Vapi's approach differs from some competitors by focusing on the developer experience. The platform offers APIs and SDKs that abstract away the complexity of managing GPU infrastructure, model routing, and audio streaming. Developers can integrate voice capabilities into their applications with as few as a dozen lines of code, which has made Vapi particularly popular among fast-moving startups and mid-market companies that want voice AI without building a dedicated machine learning team. Discover more Text & Instant Messaging Company News The company's rapid ascent from seed stage to unicorn in under three years mirrors the trajectory of other AI infrastructure plays that have captured investor attention in 2026. As businesses across industries look to automate customer service, sales outreach, and internal operations, the demand for reliable, scalable voice AI infrastructure shows no signs of slowing. With fresh capital and a marquee customer in Amazon, Vapi is well-positioned to capture a significant share of this growing market. Ramo is the editorial voice of Mylistingo - an AI and technology news platform based in The Hague, Netherlands. Covering artificial intelligence, machine learning, robotics, and the future of technology, Ramo delivers accurate, accessible reporting for both general audiences and industry professionals. Every article is fact-checked and written to meet Mylistingo's strict no-fabrication editorial standards. Google has announced the 20 startups selected for its 2026 India Accelerator program, with a strong emphasis on artificial intelligence and machine learning, reflecting the maturation of India's... Apple is preparing a significant upgrade to its on-device AI capabilities that could allow future iPhones to run far more powerful machine learning models directly on the handset,...